huggingface-tokenizers

This skill quantizes LLMs to 8-bit or 4-bit precision using bitsandbytes, achieving 50-75% memory reduction with minimal accuracy loss. It's ideal for running larger models on limited GPU memory or accelerating inference, supporting formats like INT8, NF4, and FP4. The skill integrates with HuggingFace Transformers and enables QLoRA training and 8-bit optimizers.

This skill integrates Weights & Biases for comprehensive ML experiment tracking and MLOps. It automatically logs metrics, visualizes training in real-time, and manages hyperparameter sweeps and model versions. Use it to compare runs, optimize models, and collaborate within team workspaces directly from your development environment.

This skill enables fine-tuning of LLMs using TRL's reinforcement learning methods including SFT, DPO, and PPO for RLHF and preference alignment. It's designed for aligning models with human feedback and works with HuggingFace Transformers. Use it when you need to implement RLHF, optimize with rewards, or train from human preferences.

The qdrant-vector-search skill provides a high-performance vector similarity search engine for building production RAG systems. It enables fast nearest neighbor search, hybrid search with filtering, and scalable vector storage powered by Rust. Use it when you need low-latency semantic search with horizontal scaling capabilities and full data control.